Craig M. Lewis is Director and Chief Economist of the Division of Risk, Strategy, and Financial Innovation (the SEC’s think tank, also known as Risk Fin or RSFI) and is on leave as a professor of finance at Vanderbilt University. In a speech he gave in December 2012, Risk Modeling At The SEC: The Accounting Quality Model, Dr. Lewis stated that the RSFI Office of Quantitative Research is “developing cutting-edge ways to integrate data analysis into risk monitoring.” The mining of XBRL data is a key part of this work.He explained that his SEC team’s early success has “fed our ambition” about what the SEC can do to create technologically advanced, data-driven monitoring programs.

Dimensions spoke by telephone with Dr. Lewis to learn more about the accounting quality model or AQM—a set of quantitative analytics modeling tools that the SEC is designing to review filings—and about the role of XBRL in this application. The accounting quality model will search for financial statements that “appear anomalous” and will automatically flag them for review by an examiner. The model is not expected to be fully implemented until the end of 2013.

What is this new automated tool that the SEC is developing for monitoring risk and discovering accounting anomalies? How do you expect it to work?

It is a predictive model that attempts to identify firms which have made unusual accounting choices relative to their peer group. A firm has a significant amount of discretion in the way it chooses to report elements for financial statement purposes. The degree to which, let’s say, a CFO uses this discretion can have an impact on the numbers that are actually reported. For example, consider somebody who want to smooth earnings. If a firm was having a down year and felt that the actual numbers were lower than its peer group, it may seek ways to try and boost income, maybe by not recording as much bad debt expense. There is a significant amount of discretion around how someone could choose to accrue for bad debt expense. One way to do that is to recognize the type of year a firm is having. Suppose it is a bad year. A manager may simply say: “Well, it’s a bad year; let’s take something out of the accrual bank.” To do this, one would then say: “These credits look solid to us; we don’t think we’re going to lose much.” In a good year, you look at the exact same set of accounts, and you say: “You know something? A lot of these credits are likely to be unable to pay us, so we want to take a little more bad debt expense.” This allows you to make a deposit to the accrual bank. So there is a mechanism for raising income in bad years and lowering income in good years. The reason why firms are interested in doing both is that all accounting entries eventually reverse. To be able to over-report income in a particular year, you actually have to have something in the bank that you can take out and use when you need it, and firms can use the way they accrue for certain liabilities to accomplish this—or the way they recognize revenue, for that matter.

So it is very much a peer-comparison type of tool.

Yes, the way you would identify unusual accounting choices is to compare them to those of your peers, because firms that operate in the same line of business tend to have very similar accounting reporting issues and make similar choices about how they report elements. If you are an oil and gas producer, there are a lot of accounting rules about how oil and gas producers have to book income, account for reserves, etc. If you are a software manufacturer, those same rules would not apply to you, so you would not want to compare a software manufacturer against an oil and gas company.

The tool has been referred to as “RoboCop.” Does that make it sound too automated?

It is an automated process. But the RoboCop reference, I thought, seems to be based more on the idea of a fraud-detection model—the robot police coming out and busting the fraudsters—as opposed to what I was hoping it would do, which is to simply be a tool to improve the quality of financial reports. But it is a fully automated system that effectively takes a firm’s filing the day it comes in, processes it, and then keeps it in the database so that somebody who is interested in looking at a report on that company would be able to do so within 24 hours of the filing being posted on EDGAR.

Would you be able to do this if companies were not tagging their financials with XBRL?

It would not be as useful a tool as it otherwise would be. My reasoning is that the tool could be developed using commercial databases—actually, the prototype was developed around commercial databases because many companies were not required to make XBRL filings until last year. I believe that is an issue, because the commercial databases contain only a subset of the filers. To be a useful tool for the SEC, it has to be something that can be applied broadly to the entire filer space. So XBRL is critical to the development of the tool simply because it allows us to have complete coverage.

Credits: This article originally published in Dimensions newsletter (merrillcorp.com/transaction-services), April 2013, Merrill Corporation.